Intelligent personal assistant (IPA) software systems comprise software agents that can perform various tasks or services on behalf of an individual user. These tasks or services may be based on a number of factors, including: spoken word input from a user, textual input from a user, gesture input from a user, a user's geolocation, a user's preferences, a user's social contacts, and an ability to access information from a variety of online sources, such as via the World Wide Web. However, current IPA software systems have fundamental limitations in natural language processing (NLP) and natural language understanding (NLU) in practical application. Some of these challenges have been addressed in the commonly-assigned and co-pending '157 application, which describes in detail a Universal Interaction Platform (UIP) having a new paradigm for Artificial Intelligence (AI)-based interactions with various third-party services and Internet-enabled, i.e., ‘smart’ devices, wherein each target service endpoint is treated similarly to a person for the sake of interactions with the user of a UIP-enabled device or application.
Using the techniques described in the '157 application, language context and action possibilities gleaned from user commands can be constrained by identifying the specific service that the user is sending the command to before attempting to perform any NLP/NLU—thus increasing the accuracy of results and significantly reducing the amount of processing work needed to understand the commands. However, this strategy may fall short in the context of AI-enabled IPAs, wherein the user may often engage in macro-level ‘conversations’ with his or her device via a generic query to a single IPA ‘persona.’ In such situations, it becomes more complex and challenging for the IPA to reliably direct the user's commands to the appropriate data, interface, third-party service, etc.—especially when a given command may seemingly apply with equal validity to two or more known third-party interfaces or services that the IPA software agent is capable of interfacing with.
The subject matter of the present disclosure is directed to overcoming, or at least reducing the effects of, one or more of the problems set forth above. To address these and other issues, techniques that enable intelligent, generic, yet context-aware communications between a user and an AI-driven IPA are described herein.